{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tinh chỉnh Mô hình Ngôn ngữ Thị giác (VLM)\n",
    "\n",
    "Notebook này minh họa cách tinh chỉnh mô hình `HuggingFaceTB/SmolVLM-Instruct` bằng cách sử dụng `SFTTrainer` từ thư viện `trl`. Các ô của notebook sẽ chạy và tinh chỉnh mô hình. Bạn có thể chọn mức độ khó bằng cách thử các tập dữ liệu khác nhau.\n",
    "\n",
    "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n",
    "<h2 style='margin: 0;color:blue'>Bài tập: Tinh chỉnh SmolVLM với SFTTrainer</h2>\n",
    "<p>Hãy chọn một tập dữ liệu từ Hugging Face Hub và tinh chỉnh một mô hình trên đó.</p>\n",
    "<p><b>Các mức độ khó</b></p>\n",
    "<p>🐢 Sử dụng tập dữ liệu HuggingFaceM4/ChartQA để chạy huấn luyện SFT.</p>\n",
    "<p>🐕 Sử dụng mô hình đã được tinh chỉnh để tạo ra phản hồi và cải thiện dựa trên ví dụ cơ bản.</p>\n",
    "<p>🦁 Thử các tập dữ liệu khác và cho thấy sự cải thiện.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cài đặt các thư viện cần thiết trong Google Colab\n",
    "# !pip install transformers datasets trl huggingface_hub\n",
    "\n",
    "# Xác thực với Hugging Face\n",
    "from huggingface_hub import login\n",
    "login()\n",
    "\n",
    "# Để thuận tiện, bạn có thể tạo một biến môi trường chứa token Hugging Face của mình là HF_TOKEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nhập các thư viện cần thiết\n",
    "\n",
    "import torch\n",
    "import os\n",
    "from transformers import AutoProcessor, AutoModelForVision2Seq, BitsAndBytesConfig\n",
    "from transformers.image_utils import load_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n",
      "You shouldn't move a model that is dispatched using accelerate hooks.\n",
      "Some kwargs in processor config are unused and will not have any effect: image_seq_len. \n"
     ]
    }
   ],
   "source": [
    "\n",
    "device = (\n",
    "    \"cuda\"\n",
    "    if torch.cuda.is_available()\n",
    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
    ")\n",
    "\n",
    "quantization_config = BitsAndBytesConfig(load_in_4bit=True, \n",
    "                                         bnb_4bit_use_double_quant=True, \n",
    "                                         bnb_4bit_quant_type=\"nf4\", \n",
    "                                         bnb_4bit_compute_dtype=torch.bfloat16)\n",
    "model_name = \"HuggingFaceTB/SmolVLM-Instruct\"\n",
    "model = AutoModelForVision2Seq.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=quantization_config,\n",
    "    torch_dtype=torch.bfloat16,\n",
    ").to(device)\n",
    "processor = AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\")\n",
    "\n",
    "# Đặt tên cho bản tinh chỉnh để lưu và tải lên\n",
    "finetune_name = \"SmolVLM-FT-MyDataset\"\n",
    "finetune_tags = [\"smol-course\", \"module_5\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chuẩn bị tập dữ liệu\n",
    "\n",
    "Chúng ta sẽ tải một tập dữ liệu mẫu và định dạng nó để huấn luyện. Tập dữ liệu phải được cấu trúc với các cặp đầu vào-đầu ra, trong đó mỗi đầu vào là một lời nhắc (prompt) và đầu ra là phản hồi mong đợi từ mô hình.\n",
    "\n",
    "**TRL sẽ định dạng các thông báo đầu vào dựa trên các mẫu trò chuyện (chat templates) của mô hình.** Chúng cần được biểu diễn dưới dạng danh sách các từ điển với các khóa: `role` và `content`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
       "        num_rows: 28299\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
       "        num_rows: 1920\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'query', 'label', 'human_or_machine'],\n",
       "        num_rows: 2500\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tải tập dữ liệu mẫu\n",
    "from datasets import load_dataset\n",
    "\n",
    "# TODO: xác định tập dữ liệu và cấu hình của bạn bằng cách sử dụng các tham số đường dẫn và tên\n",
    "dataset_name = \"HuggingFaceM4/ChartQA\"\n",
    "ds = load_dataset(path=dataset_name)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=314x281 at 0x7D354DDE73D0>, 'query': 'What is the sum favourable  value in the year 2014 and 2015?', 'label': ['95'], 'human_or_machine': 0, 'image_size': ((314, 281),)}\n",
      "Query: What is the sum favourable  value in the year 2014 and 2015?\n",
      "Expected Answer: 95\n",
      "Model Prediction: User:<image>What is the sum favourable  value in the year 2014 and 2015?\n",
      "Answer: 91.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sample = ds['train'][3]\n",
    "\n",
    "# Hiển thị cấu trúc của một mẫu đơn\n",
    "sample['image_size'] = sample['image'].size,\n",
    "\n",
    "# Trực quan hóa hình ảnh và siêu dữ liệu liên quan\n",
    "plt.imshow(sample['image'])\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Hình ảnh biểu đồ mẫu\")\n",
    "plt.show()\n",
    "\n",
    "print(sample)\n",
    "\n",
    "# Tiền xử lý mẫu\n",
    "prompt = [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": sample[\"query\"]}]}]\n",
    "formatted_query = processor.apply_chat_template(prompt, tokenize=False)\n",
    "\n",
    "inputs = processor(\n",
    "    images=sample[\"image\"], \n",
    "    text=formatted_query, \n",
    "    return_tensors=\"pt\"\n",
    ").to(device)\n",
    "inputs = {key: val.to(device, dtype=torch.bfloat16) if val.dtype == torch.float else val.to(device) for key, val in inputs.items()}\n",
    "\n",
    "# Tạo dự đoán\n",
    "with torch.no_grad():\n",
    "    outputs = model.generate(**inputs,\n",
    "                             max_length=1600)\n",
    "\n",
    "# Giải mã dự đoán\n",
    "prediction = processor.batch_decode(outputs, skip_special_tokens=True)\n",
    "\n",
    "# Hiển thị kết quả\n",
    "print(f\"Truy vấn: {sample['query']}\")\n",
    "print(f\"Câu trả lời mong đợi: {sample['label'][0]}\")\n",
    "print(f\"Dự đoán của mô hình: {prediction[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thiết lập LoRA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 2,568,192 || all params: 2,248,841,072 || trainable%: 0.1142\n"
     ]
    }
   ],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "# Cấu hình LoRA\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.05,\n",
    "    r=8,\n",
    "    bias=\"none\",\n",
    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "# Áp dụng điều chỉnh mô hình PEFT\n",
    "peft_model = get_peft_model(model, peft_config)\n",
    "\n",
    "# In các tham số có thể huấn luyện\n",
    "peft_model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cấu hình Trình huấn luyện (Trainer)\n",
    "\n",
    "`Trainer` được định cấu hình với nhiều tham số khác nhau để kiểm soát quá trình huấn luyện. Chúng bao gồm số bước huấn luyện, kích thước batch, tốc độ học và chiến lược đánh giá. Điều chỉnh các tham số này dựa trên các yêu cầu cụ thể và tài nguyên tính toán của bạn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(examples):\n",
    "    # Mẫu thông báo hệ thống cho VLM\n",
    "    system_message = \"\"\"Bạn là một Mô hình Ngôn ngữ Thị giác chuyên giải thích dữ liệu trực quan từ hình ảnh biểu đồ.\n",
    "    Nhiệm vụ của bạn là phân tích hình ảnh biểu đồ được cung cấp và trả lời các truy vấn bằng các câu trả lời ngắn gọn, thường là một từ, số hoặc cụm từ ngắn.\n",
    "    Các biểu đồ bao gồm nhiều loại khác nhau (ví dụ: biểu đồ đường, biểu đồ cột) và chứa màu sắc, nhãn và văn bản.\n",
    "    Tập trung vào việc cung cấp các câu trả lời chính xác, ngắn gọn dựa trên thông tin trực quan. Tránh giải thích thêm trừ khi thực sự cần thiết.\"\"\"\n",
    "\n",
    "    # Khởi tạo danh sách cho đầu vào văn bản và hình ảnh\n",
    "    text_inputs = []\n",
    "    image_inputs = []\n",
    "\n",
    "    # Xử lý tất cả các ví dụ trong một vòng lặp\n",
    "    for example in examples:\n",
    "        # Định dạng cấu trúc trò chuyện cho bộ xử lý\n",
    "        formatted_example = {\n",
    "            \"messages\": [\n",
    "                {\n",
    "                    \"role\": \"system\",\n",
    "                    \"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
    "                },\n",
    "                {\n",
    "                    \"role\": \"user\",\n",
    "                    \"content\": [\n",
    "                        {\n",
    "                            \"type\": \"image\",\n",
    "                        },\n",
    "                        {\n",
    "                            \"type\": \"text\",\n",
    "                            \"text\": example[\"query\"],\n",
    "                        },\n",
    "                    ],\n",
    "                },\n",
    "            ]\n",
    "        }\n",
    "        # Áp dụng mẫu trò chuyện và loại bỏ khoảng trắng thừa\n",
    "        text_inputs.append(processor.apply_chat_template(formatted_example[\"messages\"], tokenize=False).strip())\n",
    "        \n",
    "        # Đảm bảo hình ảnh ở chế độ RGB\n",
    "        image = example[\"image\"]\n",
    "        if image.mode != 'RGB':\n",
    "            image = image.convert('RGB')\n",
    "        image_inputs.append( [image] )\n",
    "\n",
    "    # Token hóa văn bản và xử lý hình ảnh\n",
    "    batch = processor(\n",
    "        text=text_inputs,\n",
    "        images=image_inputs,\n",
    "        return_tensors=\"pt\",\n",
    "        padding=True\n",
    "    )\n",
    "\n",
    "    # Sao chép ID đầu vào cho nhãn\n",
    "    labels = batch[\"input_ids\"].clone()\n",
    "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Che các token đệm (pad token) trong nhãn\n",
    "\n",
    "    # Đảm bảo image_token được chuyển đổi thành chuỗi nếu nó là AddedToken\n",
    "    # Trong một số bộ xử lý, processor.image_token trả về một danh sách cho mỗi hình ảnh.\n",
    "    # TODO: AutoProcessor.from_pretrained(\"HuggingFaceTB/SmolVLM-Instruct\") chỉ có một?\n",
    "    image_token_id = processor.tokenizer.convert_tokens_to_ids(str(processor.image_token))\n",
    "\n",
    "    # Che các ID token hình ảnh trong nhãn\n",
    "    labels[labels == image_token_id] = -100\n",
    "\n",
    "    # Thêm nhãn trở lại batch\n",
    "    batch[\"labels\"] = labels\n",
    "\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from trl import SFTConfig, SFTTrainer\n",
    "\n",
    "# Cấu hình Trình huấn luyện\n",
    "training_args = SFTConfig(\n",
    "    output_dir=\"sft_output\",                # Thư mục để lưu mô hình\n",
    "    num_train_epochs=3,                     # số epoch huấn luyện\n",
    "    per_device_train_batch_size=1,          # kích thước batch trên mỗi thiết bị trong quá trình huấn luyện\n",
    "    gradient_accumulation_steps=16,         # số bước trước khi thực hiện lan truyền ngược/cập nhật\n",
    "    gradient_checkpointing=True,            # sử dụng checkpoint gradient để tiết kiệm bộ nhớ\n",
    "    optim=\"adamw_torch_fused\",              # sử dụng trình tối ưu hóa adamw_torch_fused\n",
    "    logging_steps=5,                        # ghi log sau mỗi 5 bước\n",
    "    save_strategy=\"epoch\",                  # lưu checkpoint sau mỗi epoch\n",
    "    learning_rate=2e-4,                     # tốc độ học, dựa trên bài báo QLoRA\n",
    "    bf16=True,                              # sử dụng độ chính xác bfloat16\n",
    "    tf32=True,                              # sử dụng độ chính xác tf32\n",
    "    max_grad_norm=0.3,                      # chuẩn gradient tối đa dựa trên bài báo QLoRA\n",
    "    warmup_ratio=0.03,                      # tỷ lệ khởi động dựa trên bài báo QLoRA\n",
    "    lr_scheduler_type=\"constant\",           # sử dụng bộ lập lịch tốc độ học không đổi\n",
    "    push_to_hub=True,                       # đẩy mô hình lên hub\n",
    "    gradient_checkpointing_kwargs = {\"use_reentrant\": False}, # sử dụng checkpointing reentrant\n",
    "    # dataloader_num_workers=16, \n",
    "    dataset_text_field=\"\",                           # cần một trường giả cho bộ đối chiếu (collator)\n",
    "    dataset_kwargs = {\"skip_prepare_dataset\": True}, # quan trọng cho bộ đối chiếu\n",
    "    remove_unused_columns = False                    # cần thiết nếu không các đặc trưng ngoại trừ nhãn sẽ bị xóa\n",
    ")\n",
    "# Khởi tạo Trình huấn luyện\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=ds[\"train\"],\n",
    "    eval_dataset=ds[\"test\"],\n",
    "    data_collator=collate_fn,\n",
    "    peft_config=peft_config,\n",
    "    tokenizer=processor.tokenizer,\n",
    ")\n",
    "\n",
    "# TODO: 🦁 🐕 điều chỉnh các tham số SFTTrainer với tập dữ liệu bạn đã chọn. Ví dụ: nếu bạn đang sử dụng tập dữ liệu `bigcode/the-stack-smol`, bạn sẽ cần chọn cột `content`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Huấn luyện mô hình\n",
    "\n",
    "Với trình huấn luyện được cấu hình, giờ đây chúng ta có thể tiến hành huấn luyện mô hình. Quá trình huấn luyện sẽ bao gồm việc lặp qua tập dữ liệu, tính toán hàm mất mát (loss) và cập nhật các tham số của mô hình để giảm thiểu hàm mất mát này."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b4e2897ddbc47fa90254bcf12ce92f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5304 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "trainer.train()\n",
    "\n",
    "# Save the model\n",
    "trainer.save_model(f\"./{finetune_name}\")\n",
    "\n",
    "# Save to the huggingface hub if login (HF_TOKEN is set)\n",
    "if os.getenv(\"HF_TOKEN\"):\n",
    "    trainer.push_to_hub(tags=finetune_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 💐  Chúc mừng bạn đã hoàn thành bài tập!\n",
    "Notebook này đã cung cấp hướng dẫn từng bước để tinh chỉnh mô hình `HuggingFaceTB/SmolVLM` bằng cách sử dụng `SFTTrainer`. Bằng cách làm theo các bước này, bạn có thể điều chỉnh mô hình để thực hiện các tác vụ cụ thể hiệu quả hơn. Nếu bạn muốn tiếp tục làm việc với khóa học này, đây là các bước bạn có thể thử:\n",
    "\n",
    "- Thử notebook này ở độ khó cao hơn\n",
    "- Cải thiện tài liệu khóa học thông qua một Issue hoặc PR."
   ]
  }
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